Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations4111
Missing cells15
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory320.2 B

Variable types

Text1
Numeric15
Categorical3

Alerts

average_takedowns_landed_per_15_minutes is highly overall correlated with takedown_accuracyHigh correlation
fighter_type is highly overall correlated with significant_strike_defence and 1 other fieldsHigh correlation
height_cm is highly overall correlated with reach_in_cm and 1 other fieldsHigh correlation
losses is highly overall correlated with winsHigh correlation
reach_in_cm is highly overall correlated with height_cm and 1 other fieldsHigh correlation
significant_strike_defence is highly overall correlated with fighter_type and 1 other fieldsHigh correlation
significant_strikes_absorbed_per_minute is highly overall correlated with significant_strikes_landed_per_minuteHigh correlation
significant_strikes_landed_per_minute is highly overall correlated with significant_strike_defence and 3 other fieldsHigh correlation
significant_striking_accuracy is highly overall correlated with fighter_type and 1 other fieldsHigh correlation
takedown_accuracy is highly overall correlated with average_takedowns_landed_per_15_minutesHigh correlation
takedown_defense is highly overall correlated with significant_strikes_landed_per_minuteHigh correlation
weight_bin is highly overall correlated with weight_in_kgHigh correlation
weight_in_kg is highly overall correlated with height_cm and 2 other fieldsHigh correlation
wins is highly overall correlated with lossesHigh correlation
fighter_type is highly imbalanced (54.1%) Imbalance
wins has 151 (3.7%) zeros Zeros
losses has 173 (4.2%) zeros Zeros
draws has 3430 (83.4%) zeros Zeros
significant_strikes_landed_per_minute has 772 (18.8%) zeros Zeros
significant_striking_accuracy has 772 (18.8%) zeros Zeros
significant_strikes_absorbed_per_minute has 701 (17.1%) zeros Zeros
significant_strike_defence has 709 (17.2%) zeros Zeros
average_takedowns_landed_per_15_minutes has 1738 (42.3%) zeros Zeros
takedown_accuracy has 1738 (42.3%) zeros Zeros
takedown_defense has 1496 (36.4%) zeros Zeros
average_submissions_attempted_per_15_minutes has 2321 (56.5%) zeros Zeros

Reproduction

Analysis started2025-08-03 15:39:43.917970
Analysis finished2025-08-03 15:40:15.515167
Duration31.6 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

name
Text

Distinct4105
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size281.6 KiB
2025-08-03T21:10:15.811338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.106787
Min length5

Characters and Unicode

Total characters53882
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4099 ?
Unique (%)99.7%

Sample

1st rowRobert Drysdale
2nd rowDaniel McWilliams
3rd rowDan Molina
4th rowPaul Ruiz
5th rowCollin Huckbody
ValueCountFrequency (%)
chris 56
 
0.7%
mike 50
 
0.6%
john 45
 
0.5%
silva 41
 
0.5%
josh 40
 
0.5%
joe 36
 
0.4%
jason 35
 
0.4%
justin 34
 
0.4%
matt 31
 
0.4%
nick 30
 
0.4%
Other values (4765) 7976
95.2%
2025-08-03T21:10:16.332467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5401
 
10.0%
e 4409
 
8.2%
4267
 
7.9%
o 3675
 
6.8%
i 3645
 
6.8%
n 3601
 
6.7%
r 3327
 
6.2%
l 2358
 
4.4%
s 2136
 
4.0%
t 1662
 
3.1%
Other values (46) 19401
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5401
 
10.0%
e 4409
 
8.2%
4267
 
7.9%
o 3675
 
6.8%
i 3645
 
6.8%
n 3601
 
6.7%
r 3327
 
6.2%
l 2358
 
4.4%
s 2136
 
4.0%
t 1662
 
3.1%
Other values (46) 19401
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5401
 
10.0%
e 4409
 
8.2%
4267
 
7.9%
o 3675
 
6.8%
i 3645
 
6.8%
n 3601
 
6.7%
r 3327
 
6.2%
l 2358
 
4.4%
s 2136
 
4.0%
t 1662
 
3.1%
Other values (46) 19401
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5401
 
10.0%
e 4409
 
8.2%
4267
 
7.9%
o 3675
 
6.8%
i 3645
 
6.8%
n 3601
 
6.7%
r 3327
 
6.2%
l 2358
 
4.4%
s 2136
 
4.0%
t 1662
 
3.1%
Other values (46) 19401
36.0%

wins
Real number (ℝ)

High correlation  Zeros 

Distinct57
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.366821
Minimum0
Maximum253
Zeros151
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:16.524076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median11
Q317
95-th percentile28
Maximum253
Range253
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.3746673
Coefficient of variation (CV)0.75804991
Kurtosis110.20501
Mean12.366821
Median Absolute Deviation (MAD)5
Skewness5.4142368
Sum50840
Variance87.884388
MonotonicityNot monotonic
2025-08-03T21:10:16.714645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 254
 
6.2%
11 240
 
5.8%
9 236
 
5.7%
6 230
 
5.6%
10 219
 
5.3%
7 215
 
5.2%
13 209
 
5.1%
12 201
 
4.9%
14 191
 
4.6%
5 174
 
4.2%
Other values (47) 1942
47.2%
ValueCountFrequency (%)
0 151
3.7%
1 118
2.9%
2 109
2.7%
3 111
2.7%
4 133
3.2%
5 174
4.2%
6 230
5.6%
7 215
5.2%
8 254
6.2%
9 236
5.7%
ValueCountFrequency (%)
253 1
 
< 0.1%
101 1
 
< 0.1%
91 1
 
< 0.1%
75 1
 
< 0.1%
63 1
 
< 0.1%
60 4
0.1%
56 3
0.1%
55 1
 
< 0.1%
53 2
< 0.1%
51 3
0.1%

losses
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.726344
Minimum0
Maximum83
Zeros173
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:16.883328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q38
95-th percentile15
Maximum83
Range83
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1037684
Coefficient of variation (CV)0.8912787
Kurtosis22.236528
Mean5.726344
Median Absolute Deviation (MAD)3
Skewness2.9790012
Sum23541
Variance26.048452
MonotonicityNot monotonic
2025-08-03T21:10:17.019388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3 512
12.5%
2 476
11.6%
4 454
11.0%
1 440
10.7%
5 407
9.9%
6 328
8.0%
7 284
6.9%
8 197
 
4.8%
9 176
 
4.3%
0 173
 
4.2%
Other values (30) 664
16.2%
ValueCountFrequency (%)
0 173
 
4.2%
1 440
10.7%
2 476
11.6%
3 512
12.5%
4 454
11.0%
5 407
9.9%
6 328
8.0%
7 284
6.9%
8 197
 
4.8%
9 176
 
4.3%
ValueCountFrequency (%)
83 1
 
< 0.1%
62 1
 
< 0.1%
53 1
 
< 0.1%
42 1
 
< 0.1%
37 2
< 0.1%
35 2
< 0.1%
33 3
0.1%
32 2
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%

draws
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26441255
Minimum0
Maximum11
Zeros3430
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:17.173416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82237321
Coefficient of variation (CV)3.1101898
Kurtosis51.868527
Mean0.26441255
Median Absolute Deviation (MAD)0
Skewness6.0269944
Sum1087
Variance0.6762977
MonotonicityNot monotonic
2025-08-03T21:10:17.318279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3430
83.4%
1 486
 
11.8%
2 120
 
2.9%
3 31
 
0.8%
4 12
 
0.3%
5 11
 
0.3%
7 7
 
0.2%
6 5
 
0.1%
10 3
 
0.1%
9 2
 
< 0.1%
Other values (2) 4
 
0.1%
ValueCountFrequency (%)
0 3430
83.4%
1 486
 
11.8%
2 120
 
2.9%
3 31
 
0.8%
4 12
 
0.3%
5 11
 
0.3%
6 5
 
0.1%
7 7
 
0.2%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 3
 
0.1%
9 2
 
< 0.1%
8 2
 
< 0.1%
7 7
 
0.2%
6 5
 
0.1%
5 11
 
0.3%
4 12
 
0.3%
3 31
 
0.8%
2 120
2.9%

height_cm
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.20284
Minimum152.4
Maximum226.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:17.463121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum152.4
5-th percentile165.1
Q1172.72
median177.8
Q3182.88
95-th percentile190.5
Maximum226.06
Range73.66
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation8.5605092
Coefficient of variation (CV)0.048038006
Kurtosis0.34148838
Mean178.20284
Median Absolute Deviation (MAD)5.08
Skewness0.045701792
Sum732591.88
Variance73.282317
MonotonicityNot monotonic
2025-08-03T21:10:17.618843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
177.8 698
17.0%
182.88 437
10.6%
175.26 404
9.8%
180.34 363
8.8%
172.72 325
7.9%
170.18 318
7.7%
185.42 314
7.6%
187.96 261
 
6.3%
167.64 239
 
5.8%
190.5 214
 
5.2%
Other values (16) 538
13.1%
ValueCountFrequency (%)
152.4 5
 
0.1%
154.94 15
 
0.4%
157.48 27
 
0.7%
160.02 61
 
1.5%
162.56 95
 
2.3%
165.1 137
 
3.3%
167.64 239
5.8%
170.18 318
7.7%
172.72 325
7.9%
175.26 404
9.8%
ValueCountFrequency (%)
226.06 1
 
< 0.1%
218.44 1
 
< 0.1%
210.82 3
 
0.1%
208.28 3
 
0.1%
205.74 1
 
< 0.1%
203.2 9
 
0.2%
200.66 11
 
0.3%
198.12 20
 
0.5%
195.58 42
 
1.0%
193.04 107
2.6%

weight_in_kg
Real number (ℝ)

High correlation 

Distinct112
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.389776
Minimum47.63
Maximum349.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:17.770622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum47.63
5-th percentile56.7
Q165.77
median77.11
Q383.91
95-th percentile113.4
Maximum349.27
Range301.64
Interquartile range (IQR)18.14

Descriptive statistics

Standard deviation17.790948
Coefficient of variation (CV)0.22988758
Kurtosis15.702637
Mean77.389776
Median Absolute Deviation (MAD)11.34
Skewness2.0772124
Sum318149.37
Variance316.51784
MonotonicityNot monotonic
2025-08-03T21:10:17.938500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.11 691
16.8%
70.31 620
15.1%
83.91 493
12.0%
61.23 450
10.9%
65.77 441
10.7%
92.99 359
8.7%
56.7 288
7.0%
52.16 121
 
2.9%
120.2 69
 
1.7%
104.33 31
 
0.8%
Other values (102) 548
13.3%
ValueCountFrequency (%)
47.63 2
 
< 0.1%
51.26 2
 
< 0.1%
52.16 121
 
2.9%
56.7 288
7.0%
58.97 4
 
0.1%
61.23 450
10.9%
63.05 19
 
0.5%
64.86 3
 
0.1%
65.77 441
10.7%
67.59 1
 
< 0.1%
ValueCountFrequency (%)
349.27 1
 
< 0.1%
195.04 1
 
< 0.1%
185.97 1
 
< 0.1%
181.44 2
< 0.1%
176.9 1
 
< 0.1%
174.63 2
< 0.1%
158.76 3
0.1%
156.49 3
0.1%
151.95 1
 
< 0.1%
149.69 1
 
< 0.1%

reach_in_cm
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.31096
Minimum147.32
Maximum213.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:18.086723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum147.32
5-th percentile167.64
Q1180.34
median182.88
Q3182.88
95-th percentile195.58
Maximum213.36
Range66.04
Interquartile range (IQR)2.54

Descriptive statistics

Standard deviation7.8024615
Coefficient of variation (CV)0.042797546
Kurtosis2.3128469
Mean182.31096
Median Absolute Deviation (MAD)0
Skewness-0.31797318
Sum749480.34
Variance60.878405
MonotonicityNot monotonic
2025-08-03T21:10:18.220084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
182.88 2133
51.9%
177.8 201
 
4.9%
180.34 200
 
4.9%
185.42 194
 
4.7%
187.96 177
 
4.3%
190.5 173
 
4.2%
175.26 150
 
3.6%
172.72 136
 
3.3%
193.04 115
 
2.8%
170.18 105
 
2.6%
Other values (17) 527
 
12.8%
ValueCountFrequency (%)
147.32 2
 
< 0.1%
149.86 1
 
< 0.1%
152.4 5
 
0.1%
154.94 7
 
0.2%
157.48 16
 
0.4%
160.02 36
 
0.9%
162.56 49
1.2%
165.1 67
1.6%
167.64 82
2.0%
170.18 105
2.6%
ValueCountFrequency (%)
213.36 3
 
0.1%
210.82 3
 
0.1%
208.28 8
 
0.2%
205.74 13
 
0.3%
203.2 32
 
0.8%
200.66 44
 
1.1%
198.12 63
 
1.5%
195.58 96
2.3%
193.04 115
2.8%
190.5 173
4.2%

stance
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size259.9 KiB
Orthodox
2526 
Unknown
823 
Southpaw
560 
Switch
 
192
Open Stance
 
7

Length

Max length11
Median length8
Mean length7.7115057
Min length6

Characters and Unicode

Total characters31702
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthodox
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowOrthodox

Common Values

ValueCountFrequency (%)
Orthodox 2526
61.4%
Unknown 823
 
20.0%
Southpaw 560
 
13.6%
Switch 192
 
4.7%
Open Stance 7
 
0.2%
Sideways 3
 
0.1%

Length

2025-08-03T21:10:18.369309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T21:10:18.555346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox 2526
61.3%
unknown 823
 
20.0%
southpaw 560
 
13.6%
switch 192
 
4.7%
open 7
 
0.2%
stance 7
 
0.2%
sideways 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 6435
20.3%
t 3285
10.4%
h 3278
10.3%
O 2533
 
8.0%
d 2529
 
8.0%
x 2526
 
8.0%
r 2526
 
8.0%
n 2483
 
7.8%
w 1578
 
5.0%
k 823
 
2.6%
Other values (11) 3706
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6435
20.3%
t 3285
10.4%
h 3278
10.3%
O 2533
 
8.0%
d 2529
 
8.0%
x 2526
 
8.0%
r 2526
 
8.0%
n 2483
 
7.8%
w 1578
 
5.0%
k 823
 
2.6%
Other values (11) 3706
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6435
20.3%
t 3285
10.4%
h 3278
10.3%
O 2533
 
8.0%
d 2529
 
8.0%
x 2526
 
8.0%
r 2526
 
8.0%
n 2483
 
7.8%
w 1578
 
5.0%
k 823
 
2.6%
Other values (11) 3706
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6435
20.3%
t 3285
10.4%
h 3278
10.3%
O 2533
 
8.0%
d 2529
 
8.0%
x 2526
 
8.0%
r 2526
 
8.0%
n 2483
 
7.8%
w 1578
 
5.0%
k 823
 
2.6%
Other values (11) 3706
11.7%

significant_strikes_landed_per_minute
Real number (ℝ)

High correlation  Zeros 

Distinct698
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4375164
Minimum0
Maximum17.65
Zeros772
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:18.702410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.83
median2.33
Q33.6
95-th percentile5.715
Maximum17.65
Range17.65
Interquartile range (IQR)2.77

Descriptive statistics

Standard deviation1.990903
Coefficient of variation (CV)0.81677522
Kurtosis3.2829958
Mean2.4375164
Median Absolute Deviation (MAD)1.37
Skewness1.1152882
Sum10020.63
Variance3.9636948
MonotonicityNot monotonic
2025-08-03T21:10:18.904902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 772
 
18.8%
2.6 21
 
0.5%
2.47 18
 
0.4%
3.13 17
 
0.4%
1.47 17
 
0.4%
2.27 17
 
0.4%
1.8 17
 
0.4%
1.4 17
 
0.4%
0.87 16
 
0.4%
1.2 15
 
0.4%
Other values (688) 3184
77.5%
ValueCountFrequency (%)
0 772
18.8%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 3
 
0.1%
0.14 2
 
< 0.1%
0.16 3
 
0.1%
0.17 3
 
0.1%
0.18 4
 
0.1%
ValueCountFrequency (%)
17.65 1
< 0.1%
17.1 1
< 0.1%
14.48 1
< 0.1%
13.6 1
< 0.1%
13.13 1
< 0.1%
13 1
< 0.1%
12.29 1
< 0.1%
12.27 1
< 0.1%
12.15 1
< 0.1%
12.12 1
< 0.1%

significant_striking_accuracy
Real number (ℝ)

High correlation  Zeros 

Distinct83
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.542447
Minimum0
Maximum100
Zeros772
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:19.069286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127
median40
Q349
95-th percentile62
Maximum100
Range100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation20.398502
Coefficient of variation (CV)0.57391945
Kurtosis-0.22871485
Mean35.542447
Median Absolute Deviation (MAD)10
Skewness-0.48679388
Sum146115
Variance416.09887
MonotonicityNot monotonic
2025-08-03T21:10:19.269212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 772
 
18.8%
50 158
 
3.8%
42 142
 
3.5%
40 140
 
3.4%
41 135
 
3.3%
48 134
 
3.3%
46 132
 
3.2%
45 120
 
2.9%
39 118
 
2.9%
47 117
 
2.8%
Other values (73) 2143
52.1%
ValueCountFrequency (%)
0 772
18.8%
4 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 3
 
0.1%
10 6
 
0.1%
11 3
 
0.1%
12 6
 
0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
100 18
0.4%
92 1
 
< 0.1%
87 2
 
< 0.1%
86 1
 
< 0.1%
85 2
 
< 0.1%
83 3
 
0.1%
80 7
 
0.2%
79 2
 
< 0.1%
78 1
 
< 0.1%
77 2
 
< 0.1%

significant_strikes_absorbed_per_minute
Real number (ℝ)

High correlation  Zeros 

Distinct813
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1452055
Minimum0
Maximum52.5
Zeros701
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:19.453872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.55
median2.94
Q34.23
95-th percentile7.36
Maximum52.5
Range52.5
Interquartile range (IQR)2.68

Descriptive statistics

Standard deviation2.8485022
Coefficient of variation (CV)0.90566488
Kurtosis54.057907
Mean3.1452055
Median Absolute Deviation (MAD)1.34
Skewness4.4631217
Sum12929.94
Variance8.1139649
MonotonicityNot monotonic
2025-08-03T21:10:19.620489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 701
 
17.1%
3.2 23
 
0.6%
4.07 20
 
0.5%
3.27 19
 
0.5%
3.53 19
 
0.5%
2.53 19
 
0.5%
2.4 19
 
0.5%
3.33 18
 
0.4%
3 17
 
0.4%
4 17
 
0.4%
Other values (803) 3239
78.8%
ValueCountFrequency (%)
0 701
17.1%
0.13 3
 
0.1%
0.17 1
 
< 0.1%
0.2 2
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
0.27 3
 
0.1%
0.29 3
 
0.1%
0.3 1
 
< 0.1%
0.32 1
 
< 0.1%
ValueCountFrequency (%)
52.5 1
< 0.1%
49.41 1
< 0.1%
42 1
< 0.1%
30 1
< 0.1%
26.67 1
< 0.1%
24.62 1
< 0.1%
24.47 1
< 0.1%
22.76 1
< 0.1%
22.5 1
< 0.1%
20.69 1
< 0.1%

significant_strike_defence
Real number (ℝ)

High correlation  Zeros 

Distinct84
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.643639
Minimum0
Maximum100
Zeros709
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:19.785638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q136
median50
Q358
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.322427
Coefficient of variation (CV)0.5234644
Kurtosis-0.17854882
Mean42.643639
Median Absolute Deviation (MAD)9
Skewness-0.89296726
Sum175308
Variance498.29074
MonotonicityNot monotonic
2025-08-03T21:10:19.969290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 709
 
17.2%
50 159
 
3.9%
57 151
 
3.7%
55 146
 
3.6%
51 137
 
3.3%
56 130
 
3.2%
54 130
 
3.2%
52 127
 
3.1%
58 124
 
3.0%
53 120
 
2.9%
Other values (74) 2178
53.0%
ValueCountFrequency (%)
0 709
17.2%
4 2
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 5
 
0.1%
12 4
 
0.1%
13 3
 
0.1%
14 4
 
0.1%
15 4
 
0.1%
16 6
 
0.1%
ValueCountFrequency (%)
100 15
0.4%
94 1
 
< 0.1%
92 1
 
< 0.1%
91 1
 
< 0.1%
86 1
 
< 0.1%
85 2
 
< 0.1%
84 5
 
0.1%
83 4
 
0.1%
82 1
 
< 0.1%
81 3
 
0.1%

average_takedowns_landed_per_15_minutes
Real number (ℝ)

High correlation  Zeros 

Distinct560
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2505862
Minimum0
Maximum32.14
Zeros1738
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:20.119027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.59
Q31.94
95-th percentile4.355
Maximum32.14
Range32.14
Interquartile range (IQR)1.94

Descriptive statistics

Standard deviation1.9357159
Coefficient of variation (CV)1.5478468
Kurtosis35.300574
Mean1.2505862
Median Absolute Deviation (MAD)0.59
Skewness4.1807952
Sum5141.16
Variance3.7469961
MonotonicityNot monotonic
2025-08-03T21:10:20.269157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1738
42.3%
1 84
 
2.0%
2 63
 
1.5%
3 34
 
0.8%
2.5 20
 
0.5%
0.67 18
 
0.4%
0.5 17
 
0.4%
4 17
 
0.4%
1.5 15
 
0.4%
0.87 15
 
0.4%
Other values (550) 2090
50.8%
ValueCountFrequency (%)
0 1738
42.3%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 3
 
0.1%
ValueCountFrequency (%)
32.14 1
< 0.1%
24.11 1
< 0.1%
20.93 1
< 0.1%
19.42 1
< 0.1%
19.35 1
< 0.1%
18.87 1
< 0.1%
17.31 1
< 0.1%
15.52 1
< 0.1%
14.15 1
< 0.1%
14.01 1
< 0.1%

takedown_accuracy
Real number (ℝ)

High correlation  Zeros 

Distinct83
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.299927
Minimum0
Maximum100
Zeros1738
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:20.435807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q345
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.70098
Coefficient of variation (CV)1.0912951
Kurtosis0.1169549
Mean26.299927
Median Absolute Deviation (MAD)22
Skewness0.92034026
Sum108119
Variance823.74627
MonotonicityNot monotonic
2025-08-03T21:10:20.621902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1738
42.3%
100 222
 
5.4%
50 221
 
5.4%
33 174
 
4.2%
25 106
 
2.6%
40 99
 
2.4%
66 74
 
1.8%
42 58
 
1.4%
20 57
 
1.4%
36 54
 
1.3%
Other values (73) 1308
31.8%
ValueCountFrequency (%)
0 1738
42.3%
4 1
 
< 0.1%
5 3
 
0.1%
6 3
 
0.1%
7 7
 
0.2%
8 4
 
0.1%
9 9
 
0.2%
10 12
 
0.3%
11 20
 
0.5%
12 16
 
0.4%
ValueCountFrequency (%)
100 222
5.4%
87 1
 
< 0.1%
85 5
 
0.1%
84 1
 
< 0.1%
83 10
 
0.2%
81 1
 
< 0.1%
80 18
 
0.4%
78 3
 
0.1%
77 3
 
0.1%
76 2
 
< 0.1%

takedown_defense
Real number (ℝ)

High correlation  Zeros 

Distinct94
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.958891
Minimum0
Maximum100
Zeros1496
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:20.802548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42
Q366
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)66

Descriptive statistics

Standard deviation34.426458
Coefficient of variation (CV)0.88366115
Kurtosis-1.3432163
Mean38.958891
Median Absolute Deviation (MAD)38
Skewness0.1679989
Sum160160
Variance1185.181
MonotonicityNot monotonic
2025-08-03T21:10:21.018972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1496
36.4%
100 293
 
7.1%
50 231
 
5.6%
66 161
 
3.9%
33 109
 
2.7%
60 86
 
2.1%
75 73
 
1.8%
25 59
 
1.4%
40 55
 
1.3%
80 55
 
1.3%
Other values (84) 1493
36.3%
ValueCountFrequency (%)
0 1496
36.4%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 3
 
0.1%
11 5
 
0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
100 293
7.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
94 5
 
0.1%
93 2
 
< 0.1%
92 13
 
0.3%
91 9
 
0.2%
90 14
 
0.3%
89 6
 
0.1%
88 12
 
0.3%
Distinct99
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61009487
Minimum0
Maximum21.9
Zeros2321
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:21.235953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.7
95-th percentile2.5
Maximum21.9
Range21.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.5059243
Coefficient of variation (CV)2.4683445
Kurtosis63.161018
Mean0.61009487
Median Absolute Deviation (MAD)0
Skewness6.5590553
Sum2508.1
Variance2.267808
MonotonicityDecreasing
2025-08-03T21:10:21.435699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2321
56.5%
0.3 142
 
3.5%
0.5 141
 
3.4%
0.4 124
 
3.0%
1 123
 
3.0%
0.2 113
 
2.7%
0.8 106
 
2.6%
0.7 103
 
2.5%
0.6 102
 
2.5%
0.1 80
 
1.9%
Other values (89) 756
 
18.4%
ValueCountFrequency (%)
0 2321
56.5%
0.1 80
 
1.9%
0.2 113
 
2.7%
0.3 142
 
3.5%
0.4 124
 
3.0%
0.5 141
 
3.4%
0.6 102
 
2.5%
0.7 103
 
2.5%
0.8 106
 
2.6%
0.9 74
 
1.8%
ValueCountFrequency (%)
21.9 1
< 0.1%
21.6 1
< 0.1%
20.9 2
< 0.1%
20.4 1
< 0.1%
16.4 1
< 0.1%
14.5 1
< 0.1%
14.4 1
< 0.1%
14.3 2
< 0.1%
14.1 1
< 0.1%
13.8 1
< 0.1%

age
Real number (ℝ)

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.663099
Minimum21
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-08-03T21:10:21.602380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile29
Q135
median39
Q344
95-th percentile52
Maximum82
Range61
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8016545
Coefficient of variation (CV)0.17148571
Kurtosis0.95356877
Mean39.663099
Median Absolute Deviation (MAD)4
Skewness0.57452226
Sum163055
Variance46.262504
MonotonicityNot monotonic
2025-08-03T21:10:22.037517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 575
 
14.0%
36 381
 
9.3%
42 297
 
7.2%
44 277
 
6.7%
34 206
 
5.0%
45 189
 
4.6%
37 159
 
3.9%
33 152
 
3.7%
38 143
 
3.5%
41 141
 
3.4%
Other values (41) 1591
38.7%
ValueCountFrequency (%)
21 1
 
< 0.1%
22 1
 
< 0.1%
23 7
 
0.2%
24 7
 
0.2%
25 19
 
0.5%
26 23
 
0.6%
27 17
 
0.4%
28 57
1.4%
29 87
2.1%
30 107
2.6%
ValueCountFrequency (%)
82 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
67 2
 
< 0.1%
66 2
 
< 0.1%
64 2
 
< 0.1%
63 2
 
< 0.1%
62 6
 
0.1%
61 4
 
0.1%
60 17
0.4%

weight_bin
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing15
Missing (%)0.4%
Memory size4.9 KiB
70-80
1346 
60-70
938 
80-90
547 
90-100
452 
<60
417 
Other values (2)
396 

Length

Max length7
Median length5
Mean length5.0268555
Min length3

Characters and Unicode

Total characters20590
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90-100
2nd row80-90
3rd row90-100
4th row60-70
5th row80-90

Common Values

ValueCountFrequency (%)
70-80 1346
32.7%
60-70 938
22.8%
80-90 547
13.3%
90-100 452
 
11.0%
<60 417
 
10.1%
100-120 296
 
7.2%
120+ 100
 
2.4%
(Missing) 15
 
0.4%

Length

2025-08-03T21:10:22.202159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T21:10:22.356523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
70-80 1346
32.9%
60-70 938
22.9%
80-90 547
13.4%
90-100 452
 
11.0%
60 417
 
10.2%
100-120 296
 
7.2%
120 100
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 8423
40.9%
- 3579
17.4%
7 2284
 
11.1%
8 1893
 
9.2%
6 1355
 
6.6%
1 1144
 
5.6%
9 999
 
4.9%
< 417
 
2.0%
2 396
 
1.9%
+ 100
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8423
40.9%
- 3579
17.4%
7 2284
 
11.1%
8 1893
 
9.2%
6 1355
 
6.6%
1 1144
 
5.6%
9 999
 
4.9%
< 417
 
2.0%
2 396
 
1.9%
+ 100
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8423
40.9%
- 3579
17.4%
7 2284
 
11.1%
8 1893
 
9.2%
6 1355
 
6.6%
1 1144
 
5.6%
9 999
 
4.9%
< 417
 
2.0%
2 396
 
1.9%
+ 100
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8423
40.9%
- 3579
17.4%
7 2284
 
11.1%
8 1893
 
9.2%
6 1355
 
6.6%
1 1144
 
5.6%
9 999
 
4.9%
< 417
 
2.0%
2 396
 
1.9%
+ 100
 
0.5%

fighter_type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.8 KiB
Striker
3336 
Balanced
758 
Grappler
 
17

Length

Max length8
Median length7
Mean length7.1885186
Min length7

Characters and Unicode

Total characters29552
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrappler
2nd rowStriker
3rd rowGrappler
4th rowStriker
5th rowStriker

Common Values

ValueCountFrequency (%)
Striker 3336
81.1%
Balanced 758
 
18.4%
Grappler 17
 
0.4%

Length

2025-08-03T21:10:22.568990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T21:10:22.703585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
striker 3336
81.1%
balanced 758
 
18.4%
grappler 17
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r 6706
22.7%
e 4111
13.9%
S 3336
11.3%
t 3336
11.3%
i 3336
11.3%
k 3336
11.3%
a 1533
 
5.2%
l 775
 
2.6%
B 758
 
2.6%
n 758
 
2.6%
Other values (4) 1567
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 6706
22.7%
e 4111
13.9%
S 3336
11.3%
t 3336
11.3%
i 3336
11.3%
k 3336
11.3%
a 1533
 
5.2%
l 775
 
2.6%
B 758
 
2.6%
n 758
 
2.6%
Other values (4) 1567
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 6706
22.7%
e 4111
13.9%
S 3336
11.3%
t 3336
11.3%
i 3336
11.3%
k 3336
11.3%
a 1533
 
5.2%
l 775
 
2.6%
B 758
 
2.6%
n 758
 
2.6%
Other values (4) 1567
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 6706
22.7%
e 4111
13.9%
S 3336
11.3%
t 3336
11.3%
i 3336
11.3%
k 3336
11.3%
a 1533
 
5.2%
l 775
 
2.6%
B 758
 
2.6%
n 758
 
2.6%
Other values (4) 1567
 
5.3%

Interactions

2025-08-03T21:10:13.157561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.034838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.938054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.009620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.825597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.785579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.635270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.454512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.677468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.692821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.004014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.089241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.015974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.873725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.870266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.270088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.159233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.054598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.112729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.953486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.894140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.756729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.570789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.816393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.851801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.137270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.203580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.120656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.023483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.972279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.371740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.307237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.189783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.207313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.068869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.020532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.888731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.705894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.946163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.988350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.237234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.320343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.242773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.120030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.108698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.474498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.436212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.336726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.361912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.168980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.136921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.989910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.020005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.062941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.105048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.370484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.438390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.369901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.244488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.249537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.603010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.572525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.482683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.493990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.306270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.274488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.112565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.133824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.196737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.238925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.470552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.586896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.500354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.384668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.405560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.719618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.684590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.624429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.612760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.435389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.405758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.220465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.256186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.355816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.362720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.608940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.722761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.613108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.522829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.571615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.836350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.806482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.843829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.742635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.570935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.520926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.335054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.390494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.474412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.507614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:03.728401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.857818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.719020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.633410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.730842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.955652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:45.949229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:47.988028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.871257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.703756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.653244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.470828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.524079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.605925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.652737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.076938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:05.986892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.854124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.766417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:11.904947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.079476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.089022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.102530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:49.990536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:51.823153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.752771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.594919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.668066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.739120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.763835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.187007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.136848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:07.965572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:09.903665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.041748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.211994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.213995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.210806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.121897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.055670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:53.906277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.737357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.799333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:59.882095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.875316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.354140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.261425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.103514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.022739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.402884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.336624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.337479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.355910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.251995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.156662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.038699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:55.885015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:57.926884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.005573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:01.988659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.464585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.372417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.241283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.136654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.503355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.471214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.492312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.482925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.386285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.327265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.157111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.006434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.090301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.133672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:02.269240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.597032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.503645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.386744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.267797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.658065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.587451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.617110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.604519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.488127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.434573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.274837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.123626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.232958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.299706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:02.508963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.720498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.656927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.503559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.436132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.770844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.716164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.720944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.741352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.588828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.556151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.371878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.238362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.373208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.442972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:02.714025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.837111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.783762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.641629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.591169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:12.886593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:14.835774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:46.822340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:48.863102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:50.705432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:52.672673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:54.525134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:56.338403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:09:58.535723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:00.568112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:02.866780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:04.950693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:06.887139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:08.764235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:10.741627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-03T21:10:13.023382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2025-08-03T21:10:22.818863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ageaverage_submissions_attempted_per_15_minutesaverage_takedowns_landed_per_15_minutesdrawsfighter_typeheight_cmlossesreach_in_cmsignificant_strike_defencesignificant_strikes_absorbed_per_minutesignificant_strikes_landed_per_minutesignificant_striking_accuracystancetakedown_accuracytakedown_defenseweight_binweight_in_kgwins
age1.0000.052-0.0610.1770.1690.2580.3560.138-0.048-0.220-0.333-0.1910.191-0.038-0.1480.2290.4850.106
average_submissions_attempted_per_15_minutes0.0521.0000.4340.0610.310-0.0050.192-0.0160.2950.0530.1950.3110.0620.4200.2490.007-0.0850.313
average_takedowns_landed_per_15_minutes-0.0610.4341.0000.0000.249-0.0830.093-0.0580.3960.1150.3270.4180.0000.8630.3670.032-0.1190.300
draws0.1770.0610.0001.0000.031-0.0160.285-0.0110.070-0.026-0.0300.0010.0430.0180.0280.0000.0220.265
fighter_type0.1690.3100.2490.0311.0000.1330.0660.2760.6530.0890.4190.6990.3860.3860.4290.0970.0700.067
height_cm0.258-0.005-0.083-0.0160.1331.0000.0250.663-0.148-0.044-0.0330.0500.098-0.042-0.0500.3960.7710.000
losses0.3560.1920.0930.2850.0660.0251.0000.0390.1660.0820.0470.0290.0330.1430.1370.0540.0510.636
reach_in_cm0.138-0.016-0.058-0.0110.2760.6630.0391.000-0.103-0.050-0.0360.0390.190-0.026-0.0160.3170.5790.037
significant_strike_defence-0.0480.2950.3960.0700.653-0.1480.166-0.1031.0000.2150.5320.3760.2570.4340.4780.086-0.2210.362
significant_strikes_absorbed_per_minute-0.2200.0530.115-0.0260.089-0.0440.082-0.0500.2151.0000.6070.3430.0360.1200.2920.029-0.1230.170
significant_strikes_landed_per_minute-0.3330.1950.327-0.0300.419-0.0330.047-0.0360.5320.6071.0000.6520.1700.3730.5550.076-0.1880.308
significant_striking_accuracy-0.1910.3110.4180.0010.6990.0500.0290.0390.3760.3430.6521.0000.2450.4180.4360.077-0.0400.266
stance0.1910.0620.0000.0430.3860.0980.0330.1900.2570.0360.1700.2451.0000.1720.1880.0690.2000.037
takedown_accuracy-0.0380.4200.8630.0180.386-0.0420.143-0.0260.4340.1200.3730.4180.1721.0000.4150.063-0.0930.322
takedown_defense-0.1480.2490.3670.0280.429-0.0500.137-0.0160.4780.2920.5550.4360.1880.4151.0000.063-0.1450.350
weight_bin0.2290.0070.0320.0000.0970.3960.0540.3170.0860.0290.0760.0770.0690.0630.0631.0000.7610.045
weight_in_kg0.485-0.085-0.1190.0220.0700.7710.0510.579-0.221-0.123-0.188-0.0400.200-0.093-0.1450.7611.000-0.050
wins0.1060.3130.3000.2650.0670.0000.6360.0370.3620.1700.3080.2660.0370.3220.3500.045-0.0501.000

Missing values

2025-08-03T21:10:15.002858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-03T21:10:15.340451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

namewinslossesdrawsheight_cmweight_in_kgreach_in_cmstancesignificant_strikes_landed_per_minutesignificant_striking_accuracysignificant_strikes_absorbed_per_minutesignificant_strike_defenceaverage_takedowns_landed_per_15_minutestakedown_accuracytakedown_defenseaverage_submissions_attempted_per_15_minutesageweight_binfighter_type
0Robert Drysdale700190.5092.99182.88Orthodox0.000.00.000.07.32100.00.021.944.090-100Grappler
1Daniel McWilliams15370185.4283.91182.88Unknown3.3677.00.000.00.000.0100.021.642.080-90Striker
2Dan Molina1390177.8097.98182.88Unknown0.000.05.5860.00.000.00.020.944.090-100Grappler
3Paul Ruiz740167.6461.23182.88Unknown1.4033.01.4075.00.000.0100.020.936.060-70Striker
4Collin Huckbody820190.5083.91193.04Orthodox2.0560.02.7342.010.23100.00.020.431.080-90Striker
5Gerald Strebendt970175.2670.31182.88Orthodox0.000.04.0038.00.000.00.016.446.070-80Grappler
6Isaiah Hill571177.8070.31182.88Unknown4.8450.00.9780.00.000.066.014.539.070-80Striker
7Kenneth Seegrist470182.8883.91182.88Orthodox3.2166.01.280.00.000.040.014.442.080-90Striker
8Will Kerr930177.8070.31175.26Orthodox1.9142.06.2233.00.000.00.014.343.070-80Striker
9Neil Grove1281198.12120.20182.88Orthodox0.000.00.000.00.000.0100.014.354.0120+Grappler
namewinslossesdrawsheight_cmweight_in_kgreach_in_cmstancesignificant_strikes_landed_per_minutesignificant_striking_accuracysignificant_strikes_absorbed_per_minutesignificant_strike_defenceaverage_takedowns_landed_per_15_minutestakedown_accuracytakedown_defenseaverage_submissions_attempted_per_15_minutesageweight_binfighter_type
4101Del Hawkins22170165.1061.23182.88Orthodox1.1527.02.0146.00.000.00.00.036.060-70Striker
4102Ariel Beck450167.6456.70167.64Southpaw4.4434.04.3164.00.000.00.00.035.0<60Striker
4103Abner Lloveras2091180.3470.31182.88Unknown6.2749.06.8743.02.0020.00.00.039.070-80Striker
4104Brian Melancon730172.7277.11182.88Orthodox4.1849.03.6464.02.02100.066.00.043.070-80Striker
4105Amaury Bitetti520175.2683.91182.88Orthodox0.000.00.000.00.000.00.00.042.080-90Balanced
4106John Campetella010175.26106.59182.88Orthodox0.000.00.000.00.000.00.00.045.0100-120Balanced
4107Andre Pederneiras112172.7270.31182.88Orthodox0.000.00.000.00.000.00.00.058.070-80Balanced
4108Bryson Kamaka12201180.3477.11182.88Orthodox9.4760.012.630.00.000.0100.00.039.070-80Striker
4109Matej Penaz610190.5083.91210.82Southpaw1.2833.02.5533.00.000.00.00.029.080-90Striker
4110Pauline Macias410162.5652.16162.56Southpaw0.8029.04.6042.02.0018.00.00.037.0<60Striker